QuantizeLab MCP Server
Provides tools to inspect Hugging Face model repositories, quantize models to GGUF, publish the resulting GGUF to the user's Hugging Face account, and track the status of quantization jobs.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@QuantizeLab MCP ServerQuantize cognitivecomputations/dolphin-2.6-phi-2 to GGUF and publish it to my profile."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
QuantizeLab MCP Server
Quantize and publish Hugging Face models as GGUF — from any MCP-capable AI assistant.
This directory describes the hosted QuantizeLab MCP server. The server itself is
operated by QuantizeLab at https://quantizelab.dev/api/public/mcp. Anyone with a
QuantizeLab account can connect to it for free from their own machine — Claude,
Cursor, or any MCP client. There is no self-hosted edition: the service runs on our
side only.
Live endpoint:
https://quantizelab.dev/api/public/mcpModels: up to 33B parameters, GGUF, four quantization levels (Q4_K_M default, Q5_K_M, Q6_K, Q8_0)
Pricing: previewed in credits before anything runs; failed jobs refund automatically
Confirmation is mandatory — the AI can suggest, you decide
Quick start
Sign up at quantizelab.dev — new accounts get 10 free credits.
Open API & MCP from the sidebar and create an API key (
ql_...). It is shown once, so keep it safe.Add the server to your client (configs below).
Ask your assistant: "Quantize
cognitivecomputations/dolphin-2.6-phi-2to Q4_K_M and publish it to my profile."
Related MCP server: Universal MCP Server
Client setup
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"quantizelab": {
"url": "https://quantizelab.dev/api/public/mcp",
"headers": { "Authorization": "Bearer ql_YOUR_API_KEY" }
}
}
}Claude Code
claude mcp add quantizelab \
--transport http \
--url https://quantizelab.dev/api/public/mcp \
--header "Authorization: Bearer ql_YOUR_API_KEY"Cursor
Settings -> MCP -> Add new MCP server:
Field | Value |
Type | URL |
Name |
|
URL |
|
Header |
|
Any other MCP client
Point it at https://quantizelab.dev/api/public/mcp (Streamable HTTP / JSON-RPC 2.0)
with the Authorization: Bearer ql_... header. A plain GET returns server metadata,
so most clients discover the tools automatically.
Direct from the terminal (no AI)
MCP is plain JSON-RPC over HTTP — drive it with curl or any script. Initialize once per session, then call tools:
curl -X POST https://quantizelab.dev/api/public/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "Authorization: Bearer ql_YOUR_API_KEY" \
-d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"terminal","version":"1.0"}}}'
# then call a tool, e.g. balance
curl -X POST https://quantizelab.dev/api/public/mcp \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "Authorization: Bearer ql_YOUR_API_KEY" \
-d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_credits","arguments":{}}}'If a call returns "not initialized", send {"jsonrpc":"2.0","method":"notifications/initialized"} first.
Tools
Tool | Description | Key args |
| Current credit balance | — |
| Tier table, quantization levels, credit packs | — |
| Price an HF repo URL without starting anything |
|
| Quantize + publish — requires |
|
| Poll a submitted job |
|
Pricing (1 credit = $0.10)
Tier | Model size | Credits | USD |
1B | <=1.1B | 5 | $0.50 |
3B | <=3B | 8 | $0.80 |
8B | <=8B | 12 | $1.20 |
15B | <=15B | 15 | $1.50 |
33B | <=33B | 22 | $2.20 |
Tier prices are for the default Q4_K_M level. Q5_K_M and Q6_K cost the same; Q8_0 (highest fidelity, ~70% larger output) adds 5 credits.
Models larger than 33B are rejected before anything is charged.
Top-up packs: Starter $5 = 50 cr / Standard $10 = 100 cr / Pro $25 = 250 cr / Studio $50 = 500 cr.
Credits are deducted only when a job actually starts and are refunded automatically if dispatch fails.
Safety (enforced server-side)
quantize_modelreturns a cost preview first; only a second call withconfirm: truequeues the job.Every run goes through the same pipeline as the website: rate limit, HF token check, model preflight, abuse guard, GPU budget, atomic credit deduction, automatic refund on failure. An API key cannot bypass any of it.
Your Hugging Face token is stored encrypted server-side and never touches the client.
Keys are stored as one-way hashes — revoke any key from the API & MCP page and it stops working immediately.
Hosted and operated by QuantizeLab
The MCP server is part of the QuantizeLab service and is not released for self-hosting. Connect to the hosted endpoint — it is free for personal use.
License
This server cannot be deployed
Maintenance
Related MCP Connectors
Remote MCP for RunComfy: ComfyUI deployments, hosted models, LoRA training. 31 tools.
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
Prepaid inference for agents over hosted MCP. Chat, image, and video.
Create images & video from any MCP agent — 17 models, spend limits, one URL.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables interaction with 3D printer management systems through an MCP server, supporting STL file manipulation, slicing, and control of printers like OctoPrint, Klipper, Duet, and more.57 npm236GPL 2.0
- -licenseNot gradedqualityNot gradedmaintenanceProvides comprehensive MCP functionality with GGUF model integration, enabling resource management, dynamic tool execution, web data fetching, file operations, and system information retrieval. Supports automatic model detection from LM Studio with built-in security measures and extensible tool plugins.-
- FlicenseNot gradedqualityNot gradedmaintenanceEnables Claude and other MCP-compatible tools to communicate with OpenAI's GPT models (GPT-5, GPT-5-mini, o3) with conversation history and session management. Features advanced controls like reasoning effort settings, token tracking, and parallel conversation sessions for efficient AI workflows.5 npm-
- FlicenseNot gradedqualityDmaintenanceIntegrates local language models (like Qwen3-8B) with MCP clients, providing tools for chat, code analysis, text generation, translation, and content summarization using your own hardware.-